Recognizing IgA-class endomysial antibody equivalent binding patterns on monkey liver substrate through EfficientNet architectures and deep learning.

ABSTRACT: Deep learning offers promising potential for automating the interpretation of immunoglobulin A (IgA) endomysial antibody (EMA) tests, a critical serological test for the diagnosis of celiac disease that currently requires labor-intensive and subjective human interpretation. In this study, we employ and comprehensively evaluate the performance of the EfficientNet and EfficientNetV2 architectures in binary (positive vs negative, where all weak and strong positive signals were grouped as positive), three-class (negative, weak positive, strong positive), and four-class (negative, weak positive, strong positive and gray zone) classification scenarios using immunofluorescence images of IgA EMA equivalent (EMA-eq) tests. Our experiments on 368 clinical samples show high performance, with E

SUBMITTER: Soylu M 

PROVIDER: S-EPMC12535235 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature